Python Developer & Machine Learning Engineer with ~4 years of professional experience (~46 months at RxLogix) building scalable microservices, intelligent document processing pipelines, and Agentic AI solutions for pharmacovigilance and healthcare workflows.
- 🛠 Core Expertise: Python, Django, FastAPI, Machine Learning, Deep Learning, NLP & Agentic AI Frameworks.
- 🩺 Domain Experience: Data Scientist & Software Engineer (ex-RxLogix), specialized in handling medical documentation, adverse event processing, and custom mapping solutions.
- ☁️ Infrastructure & Search: Experienced in AWS microservices, Docker, CI/CD pipelines, and high-performance querying with Oracle and OpenSearch.
- 🟢 Open Source: Active contributor to Python open-source libraries.
Languages & Frameworks
AI, NLP & Search
Cloud, Databases & DevOps
- Engineered intelligent document processing pipelines to extract and validate structured, unstructured medical data and clinical records.
- Migrated legacy microservices to AWS infrastructure, improving system availability and processing speeds.
- Implemented Agentic AI frameworks and custom data mapping pipelines for pharmacovigilance operations.
- Worked with high-volume database engines including Oracle and OpenSearch for optimized search and retrieval.
I actively contribute to Python open-source libraries and developer tools:
- gorakhargosh/watchdog: Contributed public API documentation enhancements and parameter type definitions (View my PRs).
- py-pdf/fpdf2: Contributed text parsing and structural rendering improvements (View my PRs).
- titipata/scipdf_parser: Enhancing scientific PDF parsing and section extraction pipelines (View my PRs).
- [Medical Safety Narrative Generator (NLG)]: Automated pipeline utilizing LLMs & fine-tuned transformer models to generate natural language safety reports from structured adverse event data.
- [Intelligent Document Processing (IDP) Engine]: Multi-modal OCR & parsing pipeline extracting structured JSON schemas from unstructured medical PDFs.
- [MedDRA Automated Event Coding Engine]: Built a hybrid search architecture combining dense vector embeddings (semantic search) and sparse lexical search (BM25/OpenSearch) to automatically map verbatim adverse event terms to standardized MedDRA codes with high precision.
- Stack: Python, Django, PostgreSQL, Celery, Redis
- Summary: Event-driven backend management platform handling asynchronous task queues, stock alerts, and real-time operational analytics.
- GitHub: @prateek-dagar